Name
Abstract | ||
|---|---|---|
It is shown that both the classic and the Bayesian Cramer-Rao bounds can be obtained by minimizing the mean square error of an estimator while constraining the underlying distribution to be within a Fisher information ball. The presented results allow for some nonstandard interpretations of the Cramer-Rao bound and, more importantly, provide a template for novel bounds on the accuracy of estimators.(c) 2020 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1016/j.sigpro.2020.107917 | SIGNAL PROCESSING |
Keywords | DocType | Volume |
Cramer-Rao bound, Fisher information, Variational techniques | Journal | 182 |
ISSN | Citations | PageRank |
0165-1684 | 0 | 0.34 |
References | Authors | |
0 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Fauss | 1 | 6 | 9.05 |
| Alex Dytso | 2 | 45 | 20.03 |
| H. V. Poor | 3 | 25411 | 1951.66 |